# dataset settings
dataset_type = 'HLKTDataset'
data_root = '/home/xdata/dataset/HLKT-v5/'
img_norm_cfg = dict(
    mean=[65.957, 65.957, 65.957], std=[51.567, 51.567, 51.567], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
    # dict(type='Resize',
    #      img_scale=[(500, 500), (600, 600), (640, 640), (700, 700), (800, 800)],
    #      multiscale_mode='value',
    #      keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(640, 640),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'Annotations/detection_train.json',
        img_prefix=data_root + 'Images/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'Annotations/detection_val.json',
        img_prefix=data_root + 'Images/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'Annotations/detection_val.json',
        img_prefix=data_root + 'Images/',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
